Exploring Multisystemic Resilience among Youth of Color Exposed to Direct and Indirect Violence
Bibliographic record
Abstract
Objectives: Youth of color (YOC) are more likely to experience direct and indirect violence compared to non-Hispanic White youth. Although the negative consequences of violence exposure are well-established, less is understood about factors that may facilitate resilience. Methods: The current study utilized hierarchical linear regression modeling to examine the associations between resilience and psychological health, social support, and school engagement among YOC in the United States who have experienced violence. Participants included 75 YOC (Mage = 9.39; SD = 1.56; 84.1% Black or African American, 9.3% Biracial or Multiracial, 5.3% Hispanic, 1.3% Indigenous) who were directly or indirectly exposed to violence in the past year. They were recruited from a family justice center in the southern United States. Results: The final model was significant (F(5,74) = 21.71; p < .001) and accounted for 57% of the variance in resilience. Specifically, lower levels of direct violence exposure (β = -.18; p = .002), greater social support from friends and nonparental adults (β = .35; p < .001), and more school engagement (β = .47; p < .001) were related to higher resilience. Conclusions and Implications: Findings highlight the importance of interpersonal and school resources in facilitating resilience among YOC who have been exposed to violence. Clinicians are encouraged to connect YOC with accessible resources to foster behavioral, cognitive, and emotional involvement in school. Further, mental health care providers can assist YOC by helping them establish and maintain supportive relationships, which may enhance resilience.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".